Water removal from biodiesel/diesel blends and jet fuel using natural resin as dehydration agent
Bibliographic record
Abstract
Water removal from both biodiesel and diesel/biodiesel blends was studied using a natural removable additive, natural resin. Specifically, two different samples of biodiesel/diesel blends and one of JP8 fuel were studied before and after the blending process with the natural additive. As well as moisture concentration, the properties of density, kinematic viscosity, conductivity, flash point, and heat of combustion were also investigated. Using the proposed method of water removal improves the physicochemical properties of biodiesel/diesel fuel blends, increases the heat of combustion up to 361 J/g, reduces the moisture by ∼66 %, and reduces the conductivity down to 39.5 %. In the case of JP8 fuel decreasing moisture content increases the heat of combustion by 187 J/g, decreases the conductivity by 60 %, and establishes the moisture level within accepted values, from 68 to lower than 50 mg/kg.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".